PhD Chapter 3

Results 2/3

This series of files compile all analyses done during Chapter 3:

All analyses have been done with R 4.0.3.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

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Sources of activity considered for the analyses:

Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):

Gear Code Years Events Species
Dredge FishDred 2010-2014 21 Mactromeris polynyma
Net FishNet 2010 5 Clupea harengus, Gadus morhua
Trap FishTrap 2010-2015 1061 Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus
Bottom-trawl FishTraw 2013-2014 2 Pandalus borealis

1. Spatial variation of exposure indices

Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).

Aquaculture
## Model selected: Sph
## nugget = 0; sill = 0.00704; range = 7.01955; kappa = 0.5

City
## Model selected: Lin
## nugget = 0.00025; sill = 0.00602; range = 8.57222; kappa = 0.5

Sediment dredging
## Model selected: Exp
## nugget = 0.00021; sill = 0.02042; range = 4.52941; kappa = 0.5

Industry
## Model selected: Sph
## nugget = 1e-04; sill = 0.0072; range = 10.10924; kappa = 0.5

Sewers
## Model selected: Exp
## nugget = 0; sill = 0.03366; range = 43.15003; kappa = 0.5

Shipping
## Model selected: Lin
## nugget = 0; sill = 0.06455; range = 4.27615; kappa = 0.5

Fisheries
## Model selected: Lin
## nugget = 0; sill = 0.02461; range = 3.40362; kappa = 0.5

2. Relationships with abiotic parameters

2.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.

Aquaculture

City

Sediment dredging

Industry

Sewers

Shipping

Fisheries

Cumulative exposure

2.2. Correlation

Correlations have been calculated with Spearman’s rank coefficient.

Correlation coefficients between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture -0.439 0.167 0.477 -0.444 -0.048 -0.688 -0.784 -0.737 -0.668 -0.62 -0.772 -0.766 -0.722 -0.733 0.311 0 -0.029 0.345 0.188
city -0.155 -0.067 0.427 -0.273 -0.096 -0.246 -0.163 -0.171 0.086 -0.004 -0.154 -0.243 -0.167 -0.015 -0.108 -0.036 -0.153 -0.055 0.035
dredging 0.275 -0.084 -0.091 0.103 0.055 0.264 0.19 0.407 0.574 0.649 0.55 0.219 0.324 0.482 -0.215 -0.133 0.049 -0.13 -0.023
industry 0.159 -0.071 -0.016 0.045 0.069 0.176 0.115 0.348 0.514 0.588 0.504 0.157 0.253 0.405 -0.246 -0.115 0.053 -0.198 -0.076
sewers 0.254 -0.037 -0.313 0.268 0.249 0.609 0.581 0.654 0.694 0.591 0.707 0.579 0.689 0.689 -0.353 -0.063 0.021 -0.369 -0.174
shipping 0.456 -0.249 -0.291 0.314 -0.015 0.537 0.504 0.618 0.693 0.677 0.708 0.549 0.576 0.687 -0.19 -0.06 0.022 -0.172 -0.095
fisheries -0.495 0.202 0.378 -0.38 -0.138 -0.569 -0.542 -0.554 -0.608 -0.578 -0.587 -0.54 -0.564 -0.614 0.308 0.173 -0.064 0.222 -0.017
cumulative_exposure 0.261 -0.108 -0.114 0.161 0.053 0.28 0.158 0.306 0.441 0.464 0.397 0.209 0.327 0.405 -0.055 -0.045 -0.004 -0.065 -0.1
p-values of correlation test between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture 2.038e-06 0.08404 1.82e-07 1.453e-06 0.6241 1.878e-16 1.123e-23 1.028e-19 2.749e-15 8.733e-13 1.446e-22 4.639e-22 1.119e-18 1.952e-19 0.001047 0.999 0.7653 0.0002525 0.05113
city 0.1087 0.4921 3.982e-06 0.004225 0.3206 0.01043 0.09275 0.07674 0.3781 0.964 0.1118 0.01126 0.08362 0.8744 0.2674 0.7138 0.1134 0.5708 0.7171
dredging 0.004038 0.3876 0.3492 0.2869 0.574 0.005687 0.04861 1.217e-05 8.309e-11 2.962e-14 6.813e-10 0.02309 0.000633 1.283e-07 0.02517 0.171 0.6121 0.1789 0.8096
industry 0.1007 0.465 0.8689 0.6459 0.4811 0.06919 0.2347 0.0002275 1.3e-08 2.144e-11 2.783e-08 0.1043 0.008203 1.389e-05 0.01022 0.236 0.5892 0.03971 0.4341
sewers 0.007974 0.702 0.000962 0.004998 0.009281 2.623e-12 4.439e-11 1.762e-14 8.768e-17 1.703e-11 1.192e-17 5.084e-11 1.659e-16 1.805e-16 0.000176 0.5189 0.8284 8.325e-05 0.07195
shipping 7.165e-07 0.009324 0.002213 0.0009345 0.8743 2.146e-09 2.655e-08 1.041e-12 1.003e-16 9.205e-16 1.105e-17 7.68e-10 7.258e-11 2.359e-16 0.04853 0.5351 0.8202 0.07554 0.3296
fisheries 5.243e-08 0.03592 5.386e-05 4.872e-05 0.1551 1.322e-10 1.395e-09 5.146e-10 3.019e-12 5.829e-11 2.539e-11 1.593e-09 2.004e-10 1.555e-12 0.001162 0.07378 0.5101 0.02081 0.8638
cumulative_exposure 0.006326 0.267 0.2382 0.09549 0.5853 0.003373 0.1026 0.001274 1.82e-06 4.227e-07 2.109e-05 0.02966 0.000561 1.372e-05 0.5715 0.6442 0.9694 0.5029 0.3047

3. Relationships with benthic communities

3.1. Taxa identity

The most abundant taxa in our study area are:

  • Density: B.neotena (1969), E. integra (1158), P.grandimana (1092), Nematoda (1044) and M. calcarea (575)
  • Biomass: E. parma (biomass of 531.5), Strongylocentrotus sp. (65.3), N. incisa (58.5), M. calcarea (45.4) and S. groenlandicus (34.3)

The following graphs present the distribution of sampled phyla along index of cumulative exposure, according to density (left panel) or biomass (right).

Exposure categories are based on the exposure index: the higher the index, the lower the status. Maximum cumulative exposure is 2.01, and the five categories are from ‘bad’ to ‘high’, with 20 %, 40 %, 60 % or 80 % of the maximum exposure.

By exposure gradient

By exposure categories

Phylum mean density by group
Phylum low bad moderate high good
Annelida 15.2 28.1 36.4 29.7 16.5
Arthropoda 13.4 41.6 54.9 44.2 3.5
Cnidaria 0 0 0 0 0.5
Echinodermata 0.2 0.273 6 0.827 56.5
Mollusca 12 9.5 19.3 12.8 9.5
Nematoda 0 0.364 4.93 16.3 26.5
Nemertea 0 0.182 0 0.231 0
Sipuncula 0.4 0.455 0.333 0.154 0
Phylum mean biomass by group
Phylum low bad moderate high good
Annelida 3.2 0.927 2.23 0.732 0.121
Arthropoda 0.0221 0.0705 0.11 0.167 0.0527
Cnidaria 0 0 0 0 1.68
Echinodermata 0.00436 3.68 2.53 6.57 53.5
Mollusca 1.8 0.234 2.62 1.3 0.572
Nematoda 0 3.64e-05 0.000393 0.00069 0.00085
Nemertea 0 0.0777 0 4.23e-05 0
Sipuncula 0.0168 0.0191 0.00497 0.00878 0

3.2. Community characteristics

The following graphs present the distribution of community characteristics along index of cumulative exposure.

Exposure categories are based on the exposure index: the higher the index, the lower the status. Maximum cumulative exposure is 2.01, and the five categories are from ‘bad’ to ‘high’, with 20 %, 40 %, 60 % or 80 % of the maximum exposure.

By exposure gradient

By exposure categories

4. Regressions

4.1. Data manipulation

For the following analyses, independant variables are exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.

All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).

Correlation coefficients between exposure indices
  aquaculture city dredging industry sewers shipping fisheries
aquaculture 1 0.061 -0.355 -0.299 -0.666 -0.696 0.718
city 0.061 1 0.334 0.325 0.131 0.22 -0.201
dredging -0.355 0.334 1 0.961 0.668 0.686 -0.471
industry -0.299 0.325 0.961 1 0.691 0.598 -0.367
sewers -0.666 0.131 0.668 0.691 1 0.65 -0.581
shipping -0.696 0.22 0.686 0.598 0.65 1 -0.721
fisheries 0.718 -0.201 -0.471 -0.367 -0.581 -0.721 1

4.2. Univariate regressions

We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models). Variable selection was not needed here, as we are interested in all exposure indices.

Results of regressions (coefficients with a significant p-value for marginal tests) are shown on the table below:

Predictor S N B H J
Depth + + +
Aquaculture
City
Dredging
Industry
Sewers -
Shipping
Fisheries +
Adjusted \(R^{2}\) 0.19 0.02 0.01 0.3 0.15

Details of the regressions, with diagnostics and cross-validation, are summarized below.

Richness
## FULL MODEL
## Adjusted R2 is: 0.19
Fitting linear model: S ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.201e-16 0.08659 -3.696e-15 1
depth 0.2064 0.1019 2.026 0.04542 *
aquaculture 0.09678 0.1112 0.87 0.3864
city 0.0129 0.09722 0.1327 0.8947
dredging -0.03005 0.1119 -0.2685 0.7889
industry -0.1443 0.1349 -1.069 0.2877
sewers -0.1273 0.1421 -0.8958 0.3725
shipping 0.1156 0.1008 1.146 0.2544
fisheries 0.1715 0.09803 1.75 0.08326 
## RMSE from cross-validation: 0.9243771
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.17 1.28 1.12 1.29 1.55 1.63 1.16 1.13

Density
## FULL MODEL
## Adjusted R2 is: 0.02
Fitting linear model: N ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.655e-16 0.09512 1.739e-15 1
depth -0.2027 0.1119 -1.811 0.0731
aquaculture -0.008937 0.1222 -0.07313 0.9418
city 0.07304 0.1068 0.6839 0.4956
dredging -0.08968 0.123 -0.7293 0.4675
industry -0.18 0.1482 -1.214 0.2277
sewers 0.1425 0.1561 0.9129 0.3635
shipping -0.07591 0.1108 -0.6854 0.4947
fisheries 0.08603 0.1077 0.7989 0.4262 
## RMSE from cross-validation: 1.046077
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.17 1.28 1.12 1.29 1.55 1.63 1.16 1.13

Biomass
## FULL MODEL
## Adjusted R2 is: 0.01
Fitting linear model: B ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.167e-16 0.0957 -1.22e-15 1
depth -0.1609 0.1126 -1.429 0.1562
aquaculture -0.1455 0.1229 -1.184 0.2394
city -0.1895 0.1075 -1.764 0.08085
dredging 0.005404 0.1237 0.04368 0.9652
industry 0.202 0.1492 1.355 0.1787
sewers -0.3808 0.1571 -2.424 0.01716 *
shipping -0.1013 0.1114 -0.9091 0.3655
fisheries -0.01856 0.1083 -0.1713 0.8643 
## RMSE from cross-validation: 1.019367
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.17 1.28 1.12 1.29 1.55 1.63 1.16 1.13

Diversity
## FULL MODEL
## Adjusted R2 is: 0.3
Fitting linear model: H ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.209e-16 0.08072 3.976e-15 1
depth 0.4797 0.09496 5.052 1.999e-06 * * *
aquaculture 0.08119 0.1037 0.783 0.4355
city 0.07613 0.09063 0.84 0.4029
dredging 0.1248 0.1043 1.196 0.2345
industry -0.1781 0.1258 -1.416 0.16
sewers -0.09725 0.1325 -0.7339 0.4647
shipping 0.03423 0.09399 0.3642 0.7165
fisheries -0.03932 0.09138 -0.4303 0.6679 
## RMSE from cross-validation: 0.9119824
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.17 1.28 1.12 1.29 1.55 1.63 1.16 1.13

Evenness
## FULL MODEL
## Adjusted R2 is: 0.15
Fitting linear model: J ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.456e-17 0.08845 6.169e-16 1
depth 0.426 0.1041 4.094 8.676e-05 * * *
aquaculture 0.008203 0.1136 0.07218 0.9426
city 0.09424 0.09932 0.9488 0.345
dredging 0.1728 0.1143 1.512 0.1338
industry -0.1712 0.1379 -1.242 0.2172
sewers -0.02543 0.1452 -0.1752 0.8613
shipping -0.07669 0.103 -0.7445 0.4583
fisheries -0.1752 0.1001 -1.75 0.08326 
## RMSE from cross-validation: 1.071161
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.17 1.28 1.12 1.29 1.55 1.63 1.16 1.13

Annelida density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.13
Fitting generalized (poisson/log) linear model: annelids ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.318 0.01917 173.1 0 * * *
depth -0.3708 0.02513 -14.76 2.857e-49 * * *
aquaculture 0.09057 0.02087 4.34 1.422e-05 * * *
city 0.08297 0.01825 4.545 5.486e-06 * * *
dredging -0.09885 0.02909 -3.398 0.000678 * * *
industry -0.1923 0.03426 -5.614 1.982e-08 * * *
sewers -0.01545 0.03351 -0.4609 0.6448
shipping 0.1217 0.01908 6.377 1.801e-10 * * *
fisheries -0.07424 0.02321 -3.198 0.001382 * * 
## Unbiased RMSE from cross-validation: 36.1357
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.25 1.3 1.13 1.32 1.56 1.66 1.18 1.13

Arthropoda density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.18
Fitting generalized (poisson/log) linear model: arthropods ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.613 0.0171 211.2 0 * * *
depth -0.07319 0.01903 -3.845 0.0001204 * * *
aquaculture -0.1102 0.02448 -4.501 6.764e-06 * * *
city 0.1334 0.01526 8.741 2.315e-18 * * *
dredging -0.1074 0.02322 -4.627 3.71e-06 * * *
industry -0.6846 0.03461 -19.78 4.339e-87 * * *
sewers 0.7261 0.02801 25.92 3.838e-148 * * *
shipping -0.08575 0.01677 -5.113 3.179e-07 * * *
fisheries 0.07656 0.01652 4.635 3.57e-06 * * * 
## Unbiased RMSE from cross-validation: 89.90331
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.28 1.25 1.11 1.23 1.99 2.1 1.15 1.13

Mollusca density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.18
Fitting generalized (poisson/log) linear model: molluscs ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.468 0.03019 81.74 0 * * *
depth 0.02113 0.02988 0.7073 0.4794
aquaculture 0.1094 0.02837 3.857 0.0001147 * * *
city 0.2289 0.02321 9.866 5.873e-23 * * *
dredging -0.08464 0.0406 -2.085 0.03708 *
industry 0.2449 0.03518 6.963 3.342e-12 * * *
sewers -0.2973 0.04603 -6.458 1.062e-10 * * *
shipping -0.2754 0.04356 -6.323 2.563e-10 * * *
fisheries 0.07235 0.02435 2.971 0.002964 * * 
## Unbiased RMSE from cross-validation: 20.77984
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.19 1.29 1.2 1.5 1.52 1.5 1.21 1.1

4.3. Multivariate regression

The model selected by the DistLM procedure has a \(R^{2}\) of 0.16. Colours represent the value of the cumulative exposure index (the bluer, the higher).

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